1. Field
The disclosure is related to an object detection method and an object detection system applying a background probability model and a dynamic texture model.
2. Description of Related Art
Along with the advance of technology, environmental safety and self safety draw more and more attention. The research on video surveillance is even more emphasized. Not only the research on video surveillance and recording makes progress, but also technology of video intelligence grows up with each day. How to precisely grasp an occurrence of an event at a very moment and take corresponding actions has become a major issue in the research of the video intelligence.
In the process of video intelligence, a lack of fast accommodation to climate or natural phenomena always results in redundant detection errors and raises disturbance or even panic. Therefore, how to provide an accurate intelligent surveillance result and overcome all kinds of problems resulted from the climate and environment has become a basic requirement for the technology of video intelligence.
The ordinary detection technique usually emphasizes on the segmentation of a foreground and a background instead of paying attention to each kind of phenomenon in a crowd scene. These techniques comprise, for example: a background subtraction method that has a fast calculation speed but is easily interfered by environmental noise; a temporal differencing method that executes a difference analysis by using continual frames or frames in a fixed time interval; or an optical flow method that is able to overcome a variation of a light shadow in the environment but require considerable calculation. However, an accuracy of the detection using the temporal differencing method is easily affected under a crowd scene because of the comparison frequency. The optical flow method is unable to filter out redundant moving objects resulted from the natural phenomena. On the other hand, current academic research uses a local binary pattern (LBP) algorithm for object detection. However, in this algorithm, once the object stops moving, an accuracy of the detection reduces rapidly, which is unable to respond to a real condition.